Abstract
In the paper, characteristics of the monthly Wolf numbers time series data, containing information about individual solar cycles, are investigated by decomposition into components with two time series methods, namely, the Singular Spectrum Analysis (SSA) and Huang- Hilbert Transform (HHT). These methods do not require any a priori knowledge about analyzed data, making them information adaptive. As a result, some of the known cycles such as Schwabe-Wolf Cycle, Hale Cycle, Gleisberg Cycle, and Suess Cycle have been identified. These components and their properties are compared with each other, as well as with known characteristics of sunspot cycles.
Original language | English |
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Pages (from-to) | 110-119 |
Number of pages | 10 |
Journal | CEUR Workshop Proceedings |
Volume | 2005 |
State | Published - 1 Jan 2017 |
Event | 2nd International Workshop on Radio Electronics and Information Technologies, REIT 2 2017 - Yekaterinburg, Russian Federation Duration: 15 Nov 2017 → … |
Keywords
- Data analysis
- Empirical mode decomposition
- Information handling
- Singular spectrum analysis
- Sunspot numbers
- Time series
ASJC Scopus subject areas
- General Computer Science